REDfly: An Integrated Knowledgebase for Insect Regulatory Genomics

We provide here an updated description of the REDfly (Regulatory Element Database for Fly) database of transcriptional regulatory elements, a unique resource that provides regulatory annotation for the genome of Drosophila and other insects. The genomic sequences regulating insect gene expression—transcriptional cis-regulatory modules (CRMs, e.g., “enhancers”) and transcription factor binding sites (TFBSs)—are not currently curated by any other major database resources. However, knowledge of such sequences is important, as CRMs play critical roles with respect to disease as well as normal development, phenotypic variation, and evolution. Characterized CRMs also provide useful tools for both basic and applied research, including developing methods for insect control. REDfly, which is the most detailed existing platform for metazoan regulatory-element annotation, includes over 40,000 experimentally verified CRMs and TFBSs along with their DNA sequences, their associated genes, and the expression patterns they direct. Here, we briefly describe REDfly’s contents and data model, with an emphasis on the new features implemented since 2020. We then provide an illustrated walk-through of several common REDfly search use cases

Visualization for Validation and Improvement of Three-dimensional Segmentation Algorithms

The Berkeley Drosophila Transcription Network Project (BDTNP) is developing a suite of methods that will allow a quantitative description and analysis of three dimensional (3D) gene expression patterns in an animal with cellular resolution. An important component of this approach are algorithms that segment 3D images of an organism into individual nuclei and cells and measure relative levels of gene expression. As part of the BDTNP, we are developing tools for interactive visualization, control, and verification of these algorithms. Here we present a volume visualization prototype system that, combined with user interaction tools, supports validation and quantitative determination of the accuracy of nuclear segmentation. Visualizations of nuclei are combined with information obtained from a nuclear segmentation mask, supporting the comparison of raw data and its segmentation. It is possible to select individual nuclei interactively in a volume rendered image and identify incorrectly segmented objects. Integration with segmentation algorithms, implemented in MATLAB, makes it possible to modify a segmentation based on visual examination and obtain additional information about incorrectly segmented objects. This work has already led to significant improvements in segmentation accuracy and opens the way to enhanced analysis of images of complex animal morphologies

Visual Exploration of Three-dimensional Gene Expression Using Physical Views and Linked Abstract Views. Accepted for Publication in

Abstract—During animal development, complex patterns of gene expression provide positional information within the embryo. To better understand the underlying gene regulatory networks, the Berkeley Drosophila Transcription Network Project (BDTNP) has developed methods that support quantitative computational analysis of three-dimensional (3D) gene expression in early Drosophila embryos at cellular resolution. We introduce PointCloudXplore (PCX), an interactive visualization tool that supports visual exploration of relationships between different genes ’ expression using a combination of established visualization techniques. Two aspects of gene expression are of particular interest: 1) gene expression patterns defined by the spatial locations of cells expressing a gene and 2) relationships between the expression levels of multiple genes. PCX provides users with two corresponding classes of data views: 1) Physical Views based on the spatial relationships of cells in the embryo and 2) Abstract Views that discard spatial information and plot expression levels of multiple genes with respect to each other. Cell Selectors highlight data associated with subsets of embryo cells within a View. Using linking, these selected cells can be viewed in multiple representations. We describe PCX as a 3D gene expression visualization tool and provide examples of how it has been used by BDTNP biologists to generate new hypotheses. Index Terms—Interactive data exploration, three-dimensional gene expression, spatial expression patterns, information visualization, visualization, physical views, multiple linked views, brushing, scatter plots. Ç